Association of gamma-glutamyl transferase variability with risk of venous thrombosis

Gamma-glutamyl transferase (GGT) is a biomarker of inflammation, and is known to be associated with stroke and atrial fibrillation. Venous thromboembolism (VT), a not uncommon thrombotic disorder, shares similar mechanisms with other thrombotic disorders including these stroke and atrial fibrillation. Given these associations, we intended to investigate the potential association between variability in GGT and VT. The study included data from the National Health Insurance Service-Health Screening Cohort, comprising 1,085,105 participants with health examinations 3 or more times from 2003 to 2008. Variability indexes were the coefficient of variation, standard deviation, and variability independent of the mean. The occurrence of venous thromboembolism (VT) was defined with more than one claim of the following ICD-10 codes: deep VT (I80.2–80.3), pulmonary thromboembolism (I26), intraabdominal venous thrombosis (I81, I82.2, I82.3), or other VT (I82.8, I82.9). To determine the relationship of quartiles of GGT with incident VT risk, Kaplan–Meier survival curve and logrank test were used. Cox’s proportional hazard regression was used to investigate the risk of VT occurrence by GGT quartile (Q1–Q4). A total of 1,085,105 subjects were incorporated in the analysis, and the average follow-up was 12.4 years (interquartile range 12.2–12.6). VT occurred in 11,769 (1.08%) patients. The GGT level was measured 5,707,768 times in this stud. Multivariable analysis showed that GGT variability were positively associated with the occurrence of VT. Compared to the Q1, the Q4 showed an adjusted HR of 1.15 (95% CI 1.09–1.21, p < 0.001) when using coefficient of variation, 1.24 (95% CI 1.17–1.31, p < 0.001) when using standard deviation, and 1.10 (95% CI 1.05–1.16, p < 0.001) when using variability independent of the mean. Increased variability of GGT may be related to an increased risk of VT. Maintaining a stable GGT level would be beneficial in reducing the risk of VT.


Discussion
The main results of our study demonstrated that variability of GGT was related to an increased risk of VT. Moreover, this finding was consistent regardless of the type of VT (deep VT, pulmonary thromboembolism, intraabdominal thrombosis, and other VT).
Previous studies have shown relationships of stroke and cardiovascular disease with GGT [18][19][20] . In a metaanalysis, GGT was related with stroke, cardiovascular, and all-cause mortality 18,19 . High GGT levels had a positive linking with increased stroke risk, and the highest GGT quartile had about 1.5 times higher cardiovascular and all-cause mortality risk than the lowest quartile 18,19 . In another study of 698,937 diabetic patients without known cardiovascular disease, chronic liver disease, and heavy alcohol consumption, the risk of stroke and death increased by 6% and 23%, respectively, in the group with increased GGT variability 21 . In a general populationbased study that investigated GGT and hospitalization for heart failure, 1.16% of events occurred during 8.4 years of follow-up, and the risk of hospitalization was high in the group with high GGT variability, with an HR of 1.22 8 . Our study confirmed that the risk of venous thrombosis increased when GGT variability was high. It can be inferred that GGT oscillation is related to the occurrence of thrombotic disease due to homeostasis failure as well as an increase in GGT.
Our study demonstrated the relationship of GGT variability and VT. Moreover, the relationship was consistent in the subgroup analysis, especially in deep VT and pulmonary thromboembolism. While PTE is the one www.nature.com/scientificreports/ of the diseases with high mortality, our study suggested additional information on VT, especially deep VT and pulmonary thromboembolism occurrence. Although our study does not explain the mechanism, there are some possible hypotheses on the results of our study. GGT is involved in glutathione homeostasis 22 . Glutathione is an anti-oxidant synthesized by glutamatecysteine ligase and glutathione synthase 23 . Elevated reactive oxygen species (ROS) can cause oxidative damage to cells 24 , and glutathione has a protective effect on ROS 25 . GGT is involved in degrading extracellular glutathione and providing cysteine during synthesis of glutathione 26 . GGT elevation promotes ROS generation and causes oxidative stress 27 , which seems to be involved in the occurrence of cardiovascular disease. The development of venous thrombosis is also affected by ROS, which influence the formation and degradation of thrombus through the coagulation pathway, fibrinolysis, and effector cells including red blood cells and platelets 28,29 . Although the exact mechanism by which GGT variability causes VT is not known, it is presumed that GGT may affect the occurrence of VT as a mechanism similar to how variability in blood pressure or blood sugar adversely affects arteriosclerosis 30,31 . Blood pressure variability affects progression of atherosclerosis by increasing inflammation, mechanical stimulation of vessels, and vascular smooth muscle cell dysfunction 30 . Considering that GGT induction is increased by oxidative stress 32 , an increase in GGT variability may indicate recurrent oxidative stress.
This study had several limitations. First, there is a possibility of other confounding factors such as coagulation tests including d-dimer and C-reactive protein, which were unavailable in our dataset. Second, the study population are Korean, and the results could not be applied to other ethnicities. Third, the retrospective observational design of our study does not allow us to establish a clear causal connection and presents challenges in identifying the exact cause of GGT variability. Although our study goal was to confirm the association of VT with a fixed estimate of the GGT variability for the prior 6 years before index date, we did not consider GGT variability may change in the follow-up periods. Fourth, cerebral VT, which mainly occurs in young women, was excluded in our study because our dataset consists of individuals older than 40 years. Fifth, this study may exhibit selection bias as it exclusively includes participants who have undergone health screening examinations, potentially resulting Table 2. The risk for occurrence of venous thrombosis according to quartiles of GGT variability. Multivariable model (1) was adjusted for sex, age, body mass index, income levels, smoking, alcohol consumption, regular physical activity, hypertension, diabetes mellitus, dyslipidemia, stroke, atrial fibrillation, renal disease, cancer, antiphospholipid syndrome, osteoporotic fracture, aspartate aminotransferase, and alanine aminotransferase. Multivariable model (2) was adjusted for sex, age, body mass index, income levels, smoking, alcohol consumption, regular physical activity, hypertension, diabetes mellitus, dyslipidemia, stroke, atrial fibrillation, renal disease, cancer, antiphospholipid syndrome, osteoporotic fracture, aspartate aminotransferase, alanine aminotransferase, and mean GGT. GGT: Gamma-glutamyl Transferase, CI: confidence interval, HR: hazard ratio, Q: quartile, CV: coefficient of variation, SD: standard deviation, VIM: variability independent of the mean. www.nature.com/scientificreports/ in a sample comprised solely of healthy individuals. Lastly, diagnostic accuracy of VT with ICD-10 codes in the National Health Insurance Service-National Health Screening (NHIS-HEALS) could not be clearly presented.

Number of events
Despite the limitations, this study has some strengths. This study utilized a nationally representative data over a significant period to examine the impact of GGT variability on VT. Our findings provide compelling evidence confirming the importance of retaining a stable GGT level as a preventive measure against VT.

Conclusion
Increased GGT variability may be linked with increased risk of VT. Maintaining stable GGT level would be helpful for reducing the risk of VT. Further studies on the mechanisms responsible for the association between GGT variability and VT development are needed.

Methods
Data source. This study utilized the NHIS-HEALS cohort database from Korea. The NHIS is a governmentcontrolled insurance provider that covers 97% of Koreans. The remaining are covered by the Medical Aid program, which is also administered by the government [33][34][35] . Annual standardized health screenings are provided by NHIS. The cohort used in this study comprised randomly selected individuals between 40 and 79 years of age, who had done at least three health screenings (Dataset number: NIHS-2021-01-715) [36][37][38] . The NHIS-HEALS cohort database used in this study includes demographic data, socioeconomic status, and health screening information, as well as a claims database that contains information on diagnosis, prescription, and treatment methods. The health screening process involved measurements of weight, height, laboratory results, and lifestyle questionnaire such as smoking and alcohol history. The NHIS-HEALS does not have any role in this study. The study analysis was approved by the Institutional Review Board of Ewha Womans University College of Medicine (2021-12-038), and consent was waived. This study is performed in accordance with the Declaration of Helsinki.
Study population and variables. The participants with health examination 3 times or more between 2003 and 2008 were included from the NIHS-HEALS database (n = 1,236,589). Participants with missing data for analysis (n = 91,251) were excluded. Furtherer, participants with a previous history of VT (n = 4414) were excluded. Finally, 1,085,105 participants were investigated in this study (Fig. 2). A detailed description of the definition of variables can be found in the supplementary methods (Supplementary methods).
Definition of GGT variability. The definition of GGT variability used in this study refers to the intraindividual variability of GGT values obtained from each examination conducted during the six years preceding the index year (2009). Three variability indexes examined were coefficient of variation, SD, and variability independent of the mean. The formular for variability independent of the mean was 100 × SD/Mean beta , where beta is the regression coefficient based on the natural logarithm of the standard deviation over the natural logarithm of the mean 39 .

Study outcomes.
The primary outcome of the study was VT occurrence, which was defined as the presence of more than one claims with diagnostic codes corresponding to any of the following ICD-10 codes: [deep VT (I80.2-80.3), pulmonary thromboembolism (I26, I26.0, I26.9), intraabdominal thrombosis (I81, I82, I82.2, I82.3), other VT (I82.8, I82.9)] with code for anticoagulants and antiplatelet, based on a previous study 40 . The follow-up period was from the index date to VT occurrence, death, or December 2020, whichever came first.

Statistical analysis.
The study used the Chi-square test and analysis of variance test to compare the demographics of different groups. All GGT variability was found to have a positive linear association, confirmed by www.nature.com/scientificreports/ restricted cubic splines 41 . Kaplan-Meier survival curve along with logrank test were used to access the association of quartiles of GGT with incident VT risk. The study calculated the incidence of VT as the number of cases divided by the sum of person-years. Cox's proportional hazard regression was used to determine the risk of VT occurrence by GGT quartile, and the hazard ratio (HR) and 95% confidence interval (CI) were determined. A multivariable regression model with adjustments for several factors, including age, sex, body mass index, domestic income, regular physical activity, alcohol drinking, smoking status, and comorbidities (diabetes mellitus, hypertension, dyslipidemia, renal disease, stroke, atrial fibrillation, cancer, antiphospholipid antibody syndrome, and osteoporotic fracture), aspartate aminotransferase, and alanine aminotransferase was constructed. Shoenfeld's residuals were performed, and no departure from the proportional hazards' assumption was detected. Subgroup analysis analyses were performed to determine the risk of each kind of VT (deep VT, pulmonary thromboembolism, intraabdominal thrombosis, and other VT). Sensitivity analysis was conducted by adjusting for (1) mean GGT level in multivariable analysis, (2) coefficient of variation, SD, and variability independent of the mean according to decile instead of quartile, and (3) excluding participants with VT within 1 year from the index date to minimize the possibility of reverse causality. Statistical Analysis System software (SAS version 9.2, SAS Institute, Cary, NC) was used in statistical evaluations. A p value < 0.05 was considered statistically significant.
Ethical approval statement. The Institutional Review Board of Ewha Womans University College of Medicine (2021-12-038) approved the analysis and provided a consent waiver as the data were anonymized and freely accessible by the NHIS for study purposes.

Data availability
The data used in this study are available in the National Health Insurance Service-National Health Screening Cohort (NHIS-HEALS) database, but restrictions apply to public availability of these data used under license for the current study. Requests for access to the NHIS data can be made through the National Health Insurance Sharing Service homepage (http:// nhiss. nhis. or. kr/ bd/ ab/ bdaba 021eng. do). For access to the database, a completed application form, research proposal, and application for approval from the institutional review board should be submitted to the inquiry committee of research support in the NHIS for review. www.nature.com/scientificreports/